Artificial human-induced soil sealing has numerous negative consequences. The extent of impervious surfaces is a key indicator of the location and intensity of human activity; however, it is also proof of damage to the natural environment as a result of the sealing and modification of ecosystems. Remote sensing techniques can help detect and monitor changes in land use and cover over an extended period. However, the limited availability of consistent satellite images with high spatiotemporal resolutions covering several decades poses major challenges for achieving high overall classification accuracy. An accurate methodology for the multitemporal detection of artificial land cover classes was developed and applied to a case study of the metropolitan area of Murcia (Spain) with its challenging landscape conditions due to the frequent presence of bare soil. For this purpose, a variety of high-resolution satellite images from SPOT 5, Rapid Eye, and PlanetScope covering a period of 20 years were used. To improve the automated detection of built-up areas, the reflectance values of the images, normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and a building surface digital model were used as inputs for the supervised classification model. We applied a random forest algorithm to non-public, high-resolution images in the Google Earth Engine (GEE) as a processing environment to identify eight target land cover classes. The results show that the proposed methodology leads to a substantial improvement, after including the indices and the digital building model, in the overall accuracy (from 93.16 to 95.97%) and in all classes. This improvement was significant for the artificial classes and was particularly noticeable for the built-up areas (from 91.1 to 95.64%) because their confusion with bare soil was considerably reduced. This work demonstrates the effectiveness of the building-surface digital model as a tool for training the classification model, as it reduces uncertainty in confusion with other spectrally similar classes and its applicability to multisource imagery.

This study seeks to contribute to the definition of a no net land take policy by 2050 for Portugal's second-largest metropolitan region (AMP, Porto Metropolitan Area) while sensitising those involved in regional and local planning to the European target. Based on an assessment of land use changes in AMP and its municipalities during 2007-2018, soil sealing levels in 2018 and population evolution, the study quantifies the processes that may impart achievement of the 2050 objective and identifies the regional drivers of net land take, to support the definition of both interventions to decrease land take and soil sealing and related targets. The main contribution of the research is the exploration of new indicators in terms of soil sealing and population data to identify the potential for implementing interventions proposed by the EU Soil Strategy that do not jeopardise the maintenance of biodiversity in urban areas. The land take rate in the AMP was the highest among mainland Portugal regions. Its main drivers were the development of transport networks, industrial and commercial units and dispersed housing. The reuse and re-naturalisation of artificial land have seen little use in the region. Accordingly, the daily net land taken in the AMP (0.59 ha/day) still needs to decrease until 2050. While artificial land increased across the AMP, most of its municipalities lost population. To reverse this trend, the AMP should implement tighter control mechanisms that ensure that infrastructure and housing needs are fully met through the reuse of urban areas that are already sealed. However, given that the share of impermeable soil in most urban areas is already significantly high, there will have to be a careful choice of locations where intensifying land use will cause less environmental damage. It is concluded that the degree of soil sealing within urban areas is fundamental for deciding on the interventions to be carried out to reduce net land take and for defining a policy towards meeting the 2050 target.